Saliency threshold: a novel saliency detection model using Ising's theory on Ferromagnetism (STIF)

With the advancement of time, the computer vision systeams are focusing on mimicking the human visual system. In this manuscript, we tried to develop a model which works at improving both the detection accuracy and computation time. First, two double opponent color based features and twelve directional edge features using Gabor filter are computed. Then the most dominant feature pertaining to the salient object is extracted using principal component analysis to form the saliency map. Further, a threshold is applied on the saliency map to detect the salient object present in the image. This threshold selection is a vital procedure. We mapped the Ising model of ferromagnetism to the salient object detection problem by employing an optimization problem for this threshold selection. Experimental results show that the proposed model outperforms the existing models in terms of detection accuracy and also takes less computation time in comparison to many methods.